The Sparse Tensor Algebra Compiler

Tensor algebra is a powerful tool with applications in machine learning, data analytics, engineering, and science. When the tensors are sparse most components are zero and the resulting code must traverse sparse data structures. Today, programmers are left to write kernels for every operation, with different mixes of sparse and dense tensors in different formats. There are countless combinations and it is impossible to manually implement and optimize them all. The Tensor Algebra Compiler (TACO) automatically generates kernels for tensor algebra operations on sparse and dense tensors. Its performance is competitive with best-in-class hand-optimized sparse kernels in popular libraries, while supporting far more tensor operations. For more information, see tensor-compiler.org.

Bio: Fredrik Kjolstad is a PhD candidate at MIT advised by Saman Amarasinghe. He works on domain-specific compilers, languages, and performance engineering for sparse computing, including the TACO compiler and the Simit programming language. He believes code should shape to data.